Merge branch 'QwenLM:main' into add_ja-readme
Ikko Eltociear Ashimine
1 year ago
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GitHub
commit
81185f0b3f
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import argparse
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import json
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import os
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import pprint
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import json5
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import jsonlines
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from rouge_score import rouge_scorer
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from tqdm import tqdm
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from transformers import Agent, AutoModelForCausalLM, AutoTokenizer
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from transformers.generation import GenerationConfig
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from transformers.tools.evaluate_agent import evaluate_agent
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from transformers.trainer_utils import set_seed
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data_root_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
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'data')
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def is_callable(response, golden):
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return response['action'].strip().lower() == golden['action'].strip(
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).lower()
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def process_res(response):
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# parse response
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response += '\n' # fix not-find bug
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thought = response[:response.find('Action:')].strip()
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action = response[response.find('Action:') +
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len('Action:'):response.find('Action Input:')].strip()
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action_input = response[response.find('Action Input:') +
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len('Action Input:'):response.find('Observation:'
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)].strip()
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#TODO: This parsing result is incorrect if the response contains multiple Actions. To be fixed in the future.
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observation = response[response.find('Observation:') +
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len('Observation:'):response.rfind('Thought:'
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)].strip()
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thought_last = response[response.rfind('Thought:') +
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len('Thought:'):response.find('Final Answer:'
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)].strip()
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final_answer = response[response.find('Final Answer:') +
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len('Final Answer:'):].strip()
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try:
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action_input = json.dumps(json5.loads(action_input),
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ensure_ascii=False,
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sort_keys=True)
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except:
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# print("JSON Load Error:", action_input)
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pass
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res_dict = {
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'thought': thought,
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'action': action,
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'action_input': action_input,
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'observation': observation,
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'thought_last': thought_last,
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'final_answer': final_answer
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}
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return res_dict
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class _DummyTokenizer:
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def tokenize(self, text: str):
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return text.split()
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def _get_tokenized_string(tokenizer, text_list):
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token_ids_list, tokenized_string_list = [], []
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for text in text_list:
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assert tokenizer is not None
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token_ids = tokenizer.encode(text)
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tokens_bytes = tokenizer.convert_ids_to_tokens(token_ids)
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tokens = [
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token.decode('utf-8', errors='replace') for token in tokens_bytes
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]
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tokenized_string = ' '.join(tokens)
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token_ids_list.append(token_ids)
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tokenized_string_list.append(tokenized_string)
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return token_ids_list, tokenized_string_list
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def eval_action(job):
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response = job['gen'][0]
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golden = job['response']
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if 'Action:' in response:
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response, golden = process_res(response), process_res(golden)
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if is_callable(response, golden):
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return True
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return False
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def eval_action_input(job, tokenizer):
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response = job['gen'][0]
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golden = job['response']
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response, golden = process_res(response), process_res(golden)
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query = job['prompt']
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job = {}
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job['prompt'] = query
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job['gen'] = response['action_input']
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job['response'] = golden['action_input']
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job['_gen_tok'], job['_gen_tok_str'] = _get_tokenized_string(
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tokenizer, [response['action_input']])
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job['_reference_tok'], job['_reference_tok_str'] = _get_tokenized_string(
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tokenizer, [golden['action_input']])
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scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'],
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tokenizer=_DummyTokenizer())
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score = scorer.score(job['_reference_tok_str'][0], job['_gen_tok_str'][0])
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rouge = score['rougeL'].fmeasure
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return rouge
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class QWenAgent(Agent):
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"""
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Agent that uses QWen model and tokenizer to generate code.
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Example:
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```py
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agent = QWenAgent()
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agent.run("Draw me a picture of rivers and lakes.")
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```
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"""
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def __init__(self,
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chat_prompt_template=None,
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run_prompt_template=None,
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additional_tools=None,
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tokenizer=None,
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model=None):
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if tokenizer and model:
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self.tokenizer = tokenizer
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self.model = model
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else:
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checkpoint = 'Qwen/Qwen-7B-Chat'
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self.tokenizer = AutoTokenizer.from_pretrained(
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checkpoint, trust_remote_code=True)
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self.model = AutoModelForCausalLM.from_pretrained(
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checkpoint, device_map='auto',
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trust_remote_code=True).cuda().eval()
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self.model.generation_config = GenerationConfig.from_pretrained(
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checkpoint, trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
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self.model.generation_config.do_sample = False # greedy
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super().__init__(
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chat_prompt_template=chat_prompt_template,
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run_prompt_template=run_prompt_template,
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additional_tools=additional_tools,
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)
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def generate_one(self, prompt, stop):
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# "Human:" 和 "Assistant:" 曾为通义千问的特殊保留字，需要替换为 "_HUMAN_:" 和 "_ASSISTANT_:"。这一问题将在未来版本修复。
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prompt = prompt.replace('Human:',
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'_HUMAN_:').replace('Assistant:',
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'_ASSISTANT_:')
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stop = [
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item.replace('Human:', '_HUMAN_:').replace('Assistant:',
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'_ASSISTANT_:')
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for item in stop
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]
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result, _ = self.model.chat(self.tokenizer, prompt, history=None)
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for stop_seq in stop:
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if result.endswith(stop_seq):
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result = result[:-len(stop_seq)]
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result = result.replace('_HUMAN_:',
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'Human:').replace('_ASSISTANT_:', 'Assistant:')
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return result
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def load_models_tokenizer(args):
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tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path,
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trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(args.checkpoint_path,
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device_map='auto',
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trust_remote_code=True,
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bf16=True,
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use_flash_attn=True).eval()
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model.generation_config = GenerationConfig.from_pretrained(
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args.checkpoint_path, trust_remote_code=True)
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model.generation_config.do_sample = False # use greedy decoding
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return model, tokenizer
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def load_jobs(filename):
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jobs = []
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with jsonlines.open(os.path.join(data_root_path, filename),
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mode='r') as reader:
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for job in reader:
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jobs.append(job)
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return jobs
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def react_inference(filename, model, tokenizer):
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filename_cache = filename + '.cache'
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if os.path.exists(os.path.join(data_root_path, filename_cache)):
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jobs = load_jobs(filename=filename_cache)
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print('Loaded from', filename_cache)
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else:
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with open(os.path.join(data_root_path, filename_cache), 'w') as f:
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jobs = load_jobs(filename=filename)
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print('Inference:', filename)
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for job in tqdm(jobs):
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response, history = model.chat(tokenizer,
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job['prompt'],
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history=None)
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job['gen'] = [response]
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f.writelines(json.dumps(job, ensure_ascii=False) + '\n')
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print(filename_cache, 'is saved.')
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return jobs
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def main(args):
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print('loading model weights')
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if args.checkpoint_path is not None:
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model, tokenizer = load_models_tokenizer(args)
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else:
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model, tokenizer = None, None
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print('model loaded')
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result = {}
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# eval react positive
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if args.eval_react_positive:
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print('eval react positive ...')
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acc_count = 0
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rouge_mean = 0
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jobs = react_inference(filename=args.eval_react_positive_filename,
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model=model,
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tokenizer=tokenizer)
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for job in jobs:
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if eval_action(job):
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acc_count += 1
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rouge = eval_action_input(job, tokenizer)
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rouge_mean += (rouge / len(jobs))
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scores = {
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'action_right_rate': acc_count / len(jobs),
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'action_input_rouge': rouge_mean,
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}
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result.update({'react_positive': scores})
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# eval react negative
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if args.eval_react_negative:
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print('eval react negative ...')
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bad_count = 0
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jobs = react_inference(filename=args.eval_react_negative_filename,
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model=model,
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tokenizer=tokenizer)
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for job in jobs:
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if '\nAction:' in job['gen'][0]:
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bad_count += 1
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scores = {'bad_rate': bad_count / len(jobs)}
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result.update({'react_negative': scores})
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# eval hfagent
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if args.eval_hfagent:
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print('eval hfagent ...')
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agent = QWenAgent(model=model, tokenizer=tokenizer)
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scores = evaluate_agent(agent, verbose=False, return_errors=False)
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result.update({'hfagent': scores})
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pp = pprint.PrettyPrinter(indent=4)
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pp.pprint(result)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Test HF checkpoint.')
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parser.add_argument('-c',
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'--checkpoint-path',
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type=str,
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help='Checkpoint path',
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default='Qwen/Qwen-7B-Chat')
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parser.add_argument('-s',
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'--seed',
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type=int,
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default=1234,
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help='Random seed')
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"""Provide extra arguments required for tasks."""
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group = parser.add_argument_group(title='Evaluation options')
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group.add_argument('--eval-react-positive',
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action='store_true',
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default=False,
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help='Eval react positive.')
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group.add_argument('--eval-react-positive-filename',
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type=str,
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default='exam_plugin_v1_react_positive.jsonl',
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help='Eval react positive filename.')
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group.add_argument('--eval-react-negative',
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action='store_true',
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default=False,
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help='Eval react negative.')
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group.add_argument('--eval-react-negative-filename',
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type=str,
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default='exam_plugin_v1_react_negative.jsonl',
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help='Eval react negative filename.')
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group.add_argument('--eval-hfagent',
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action='store_true',
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default=False,
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help='Eval hfagent.')
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args = parser.parse_args()
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set_seed(args.seed)
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main(args)
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